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arXiv:2606.10507v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated strong capabilities as autonomous agents across a wide range of tasks, their performance often degr

agentsarxiv-cs-ai

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HMAF: A Hierarchical Multi-Slot GD-RTB Allocation Framework

DGX agent

arXiv:2606.09896v1 Announce Type: cross Abstract: In modern online advertising platforms, Guaranteed Delivery (GD) contracts coexist and bid with Real-Time Bidding (RTB) auctions. Recent approaches ei

researcharxiv-cs-ai

10 Jun 2026

How can we assess human-agent interactions? Case studies in software agent design

arXiv:2510.09801v3 Announce Type: replace Abstract: While benchmarks measure the accuracy of LLM-powered agents, they mostly assume full automation, failing to represent the collaborative nature of re

How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMs

arXiv:2606.10646v1 Announce Type: cross Abstract: Token-level credit assignment remains a key obstacle for reinforcement learning (RL) in large language models (LLMs), where RL recipes typically treat

researcharxiv-cs-cl

Human-AI Coordination Zones: A Framework for Designing Human-in-the-Loop Experiences with Agentic AI

arXiv:2606.09848v1 Announce Type: cross Abstract: As generative and agentic AI becomes embedded in everyday products, practitioners face a persistent challenge: how to design human-AI coordination --

Human-AI Teaming Through the Lens of Calibration

arXiv:2606.10906v1 Announce Type: cross Abstract: We study models for human-AI teaming through the lens of statistical calibration. We assume the team consists of an AI model and human -- both of whic

Local Ai

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers

arXiv:2606.09960v1 Announce Type: cross Abstract: We present HydraCIL, a decoupled continual learning model based on prototype-guided multi-head classifiers, targeting sustainable deployment in embedd

local-aiarxiv-cs-ai

Hyperbolic Neural Population Geometry Benefits Computation

arXiv:2606.10238v1 Announce Type: cross Abstract: Neural population geometry shapes downstream computation. Recent empirical findings in neurobiology suggest that a hyperbolic structure underlies popu

Hyperparameter Learning for Latent Factorization of Tensors for Representation Learning to Large-scale Dynamic Weighted Directed Network

model-releasesarxiv-cs-lg

arXiv:2606.09880v1 Announce Type: new Abstract: Large-scale dynamic weighted directed networks (DWDNs) are widely used to model time-varying interactions among nodes. Latent factorization of tensors (

IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder

arXiv:2606.11096v1 Announce Type: new Abstract: Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing s

safetyarxiv-cs-cv

IDP-Bench: Benchmarking ability of LLMs to protect personal information in interdependent privacy contexts

arXiv:2606.09908v1 Announce Type: cross Abstract: Large language models (LLMs) are becoming widely deployed as personal AI assistants with access to sensitive user data, making privacy a major challen

IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation

arXiv:2606.10818v1 Announce Type: cross Abstract: Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting o

safetyarxiv-cs-cv

Agents

Impatient Users Confuse AI Agents: High-fidelity Simulations of Human Traits for Testing Agents

arXiv:2510.04491v3 Announce Type: replace Abstract: Despite rapid progress in building conversational AI agents, robustness is still largely untested. Small shifts in user behavior, such as being more

agentsarxiv-cs-ai

Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization

arXiv:2606.10068v1 Announce Type: cross Abstract: Hyperparameter Optimization (HPO) is essential for building high-performing ML/DL models, yet conventional optimizers often struggle in high-dimension

Improved Representation of Matrix Lie Group Operations through Tensor Notation

arXiv:2606.10289v1 Announce Type: new Abstract: Several recent papers have demonstrated the utility of using Lie groups within estimation problems, yielding improved accuracy and consistency. This pap

researcharxiv-cs-ro

Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction

arXiv:2606.10571v1 Announce Type: cross Abstract: Adversarial examples reveal vulnerabilities in Vision-Language Pre-training (VLP) models and provide insights for improving robustness. A key property

Local Ai

Improving PET/CT-Based Whole-Body Lesion Segmentation Using Prediction Uncertainty-Augmented Models

arXiv:2606.10115v1 Announce Type: new Abstract: Accurate lesion segmentation from whole-body Positron Emission Tomography (PET)/Computed Tomography (CT) scans is essential for cancer staging and treat

local-aiarxiv-cs-cv

Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding

arXiv:2606.10892v1 Announce Type: cross Abstract: To showcase products, merchants often incur substantial costs creating high-quality display images. Foreground Conditioned Outpainting (FCO) meets thi

In Defense of Cosine Similarity: Normalization Eliminates the Gauge Freedom

arXiv:2602.19393v2 Announce Type: replace Abstract: Steck, Ekanadham, and Kallus [arXiv:2403.05440] demonstrate that cosine similarity of learned embeddings from matrix factorization models can be ren

researcharxiv-cs-lg

In Defense of Information Leakage in Concept-based Models

arXiv:2606.10669v1 Announce Type: cross Abstract: Concept-based models (CMs), deep neural networks that ground their predictions on representations aligned with human-understandable concepts (e.g., 'r

tutorialsarxiv-cs-ai

Agents

Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory

arXiv:2606.10677v1 Announce Type: new Abstract: Long-term LLM agents need persistent memory that can track changing facts and provide relevant evidence across sessions. Existing memory systems often s

agentsarxiv-cs-ai

Influence Dynamics and Stagewise Data Attribution

arXiv:2510.12071v2 Announce Type: replace Abstract: Current training data attribution (TDA) methods treat the influence one sample has on another as static, but neural networks learn in distinct stage

tutorialsarxiv-cs-lg

Applications

Information-Preserving Continuous Occupancy Mapping with Variance-Weighted Submap Joining

arXiv:2606.10442v1 Announce Type: new Abstract: Large-scale SLAM remains challenging due to accumulated trajectory drift and the increasing computational cost of maintaining global consistency. Submap

applicationsarxiv-cs-ro

Informed Asymmetric Actor-Critic: Leveraging Privileged Signals Beyond Full-State Access

arXiv:2509.26000v3 Announce Type: replace Abstract: Asymmetric reinforcement learning leverages privileged information available during training to improve learning under partial observability. Existi

safetyarxiv-cs-lg

Inside the Latent Flow: Causal Deciphering of Attention Dynamics in Audio Separation Foundation Models

arXiv:2606.10046v1 Announce Type: cross Abstract: Flow-matching transformers achieve strong audio separation, yet their attention dynamics are opaque. We adapt established causal-intervention principl

Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune

arXiv:2606.10392v1 Announce Type: new Abstract: Financial named-entity recognition (NER) is essential for translating unstructured financial reports and news into structured knowledge graphs. However,

Integral Field Unit Spectroscopy with One Fiber

arXiv:2606.10197v1 Announce Type: cross Abstract: Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution. However, i

Integrated Real-Time Motion Tracking and AI Analysis for Athletic Performance Optimization

arXiv:2606.09842v1 Announce Type: cross Abstract: Applying Human Pose Estimation (HPE) in real world environments remains a challenging task, this paper explores and surveys real time HPE approaches a

tutorialsarxiv-cs-ai

Integrating Biological-Informed Recurrent Neural Networks for Glucose-Insulin Dynamics Modeling

arXiv:2503.19158v3 Announce Type: replace Abstract: Type 1 Diabetes (T1D) management is a complex task due to many variability factors. Artificial Pancreas (AP) systems have alleviated patient burden

researcharxiv-cs-lg

Local Ai

Integrating Local and Global Entropy for Uncertainty Quantification in LLMs

arXiv:2606.09875v1 Announce Type: cross Abstract: Large language models hallucinate confidently, making uncertainty quantification (UQ) essential for reliable deployment. Existing methods rely predomi

local-aiarxiv-cs-ai

Integrating Out, Twice:The Open-System Case That Neural-Network Ensemble Theory Is Missing

arXiv:2606.09950v1 Announce Type: new Abstract: Averaging a neural network over its random parameters and marginalizing a Gaussian sector are the same operation, the Schur complement of the eliminated

researcharxiv-cs-lg

IntentKV: Cross-Turn Intent-Aware KV Cache Pruning for Agent Inference

arXiv:2606.09916v1 Announce Type: cross Abstract: Multi-turn LLM agents fan short queries into long trajectories of tool calls, search results, and intermediate reasoning. Both KV memory and KV read b

Interactions Between Crosscoder Features: A Compact Proofs Perspective

arXiv:2606.09940v1 Announce Type: cross Abstract: Dictionary learning methods like Sparse Autoencoders (SAEs) and crosscoders attempt to explain a model by decomposing its activations into independent

Interpretable deep convolutional model for nonlinear multivariate time series in complex systems

model-releasesarxiv-cs-lg

arXiv:2501.04339v2 Announce Type: replace-cross Abstract: We introduce the Deep Convolutional Interpreter for Time Series (DCIts), a deep-learning architecture for nonlinear multivariate time series t

Interpretable Temporal Facial-Region Motion Analysis for In-the-Wild Parkinson's Disease Video Classification

model-releasesarxiv-cs-cv

arXiv:2606.10088v1 Announce Type: new Abstract: Reduced facial expressivity is a common motor manifestation of Parkinson's disease (PD), often described as hypomimia or facial bradykinesia. This paper

Interpreting and Steering a Text-to-Speech Language Model with Sparse Autoencoders

arXiv:2606.10029v1 Announce Type: cross Abstract: Language models increasingly serve as the backbone of text-to-speech (TTS) systems, yet we understand little about the representations they build when

Inverse Probability Weighting and Age-of-Information Aggregation for Decentralized Federated Learning under Partial Reception

arXiv:2606.10774v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) over lossy wireless networks faces two key challenges: selection bias, where updates from poor-quality links are

safetyarxiv-cs-lg

inversedMixup: Data Augmentation via Inverting Mixed Embeddings

arXiv:2601.21543v3 Announce Type: replace Abstract: Mixup generates augmented samples by linearly interpolating inputs and labels with a controllable ratio. However, since it operates at the latent em

researcharxiv-cs-cl

IPSM-Bench: A New Intermediate Phase Segmentation Benchmark in Microstructure Images of Zinc-Based Absorbable Biomaterials

model-releasesarxiv-cs-cv

arXiv:2606.11001v1 Announce Type: new Abstract: Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characte

Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-exorpdfstring{elta}{delta} Alignment

arXiv:2606.10632v1 Announce Type: cross Abstract: Lipschitz-style individual fairness formalizes the idea that semantically similar examples should receive similar predictions, but its evaluation in m

Is Task-Specific Training Necessary for Anomaly Detection?

arXiv:2601.22763v3 Announce Type: replace Abstract: Current state-of-the-art multi-class unsupervised anomaly detection (MUAD) methods rely on training encoder--decoder models to reconstruct anomaly-f

researcharxiv-cs-cv

iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision

model-releasesarxiv-cs-cv

arXiv:2606.10136v1 Announce Type: new Abstract: Semantic segmentation in remote sensing requires costly pixel-level annotations, and nearly every problem demands a new dataset since models rarely tran

It Takes One to Bias Them All: Breaking Bad with One-Shot GRPO

arXiv:2606.10931v1 Announce Type: new Abstract: Warning: This paper contains several toxic and offensive statements. Modern large language models (LLMs) are typically aligned through large-scale post-

safetyarxiv-cs-cl

Ito maps for any-step SDEs

arXiv:2606.11156v1 Announce Type: cross Abstract: Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics. These methods rely on learning f

tutorialsarxiv-cs-lg

Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs

arXiv:2606.10852v1 Announce Type: cross Abstract: LLM deception is often evaluated through direct markers such as fabricated claims, explicit lies, or strategic concealment. However, many real-world m

JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks

model-releasesarxiv-cs-lg

arXiv:2606.09964v1 Announce Type: cross Abstract: The NISQ era places stringent constraints on quantum computation, where noise and decoherence fundamentally limit performance. In classical deep learn

JOIN: Anchor-Grasp-Conditioned Joining via Opposition, Inference, and Navigation for Bimanual Assistive Manipulation

arXiv:2606.11151v1 Announce Type: new Abstract: Assistive mobility and manipulation platforms have received increasing attention as a means of restoring independence to individuals with disabilities.

researcharxiv-cs-ro

Applications

K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling

arXiv:2606.10820v1 Announce Type: cross Abstract: Autoregressive (AR) language modeling is the dominant paradigm for text generation, yet its sequential token-by-token decoding makes inference memory-

applicationsarxiv-cs-ai